\(^\star \) Work done during internship at China Mobile Research Institute.Speech enhancement (SE) aims to improve speech quality. Many researchers introduce intuitive signal processing ideas to deep learning [1]}, [2]}. [3]} explores an SE model combining signal processing and deep learning, but simple structure limits its performance. [4]} introduces the complex operations, which improve the performance but with less auditory characteristic. [5]}, [6]} introduce the auditory feature, but spectra with wide frequency bandwidth degrade the performance. [7]} verifies the effectiveness of the hearing pipeline structure. [8]} verifies the necessity of modeling the harmonic. Inspired by these works, we proposed the HGCN for SE [9]}. In HGCN, a high-resolution harmonic integration algorithm is proposed to predict the harmonic locations. Then the locations are used as a gate to help the subsequent module compensate for the result of the coarse enhancement module to obtain a refined result. To make the enhancement more consistent with human hearing, we previously proposed a loss function based on auditory loudness power compression (APC-SNR) [10]}. Both HGCN and APC-SNR have proved their effectiveness for SE.
